Determinants of heart rate variability

Objectives. This study sought to examine clinical determinants of heart rate variability and to report normative reference values for eight heart rate variability measures.

Background. Although the clinical implications of heart rate variability have been described, clinical determinants and normative values of heart rate variability measures have not been studied systematically in a large community-based population.

Methods. The first 2 h of ambulatory electrocardiographic recordings obtained in Framingham Heart Study subjects attending a routine examination were reprocessed for heart rate variability. Recordings with transient or persistent nonsinus rhythm, premature beats >10% of total beats, <1-h recording time or processed time <50% of recorded time were excluded; subjects receiving antiarrhythmic medications also were excluded. Among five frequency domain and three time domain measures that were obtained, low frequency power (0.04 to 0.15 Hz), high frequency power (0.15 to 0.40 Hz) and the standard deviation of total normal RR intervals based on 2-h recordings were selected for the principal analyses. Variables with potential physiologic effects or possible technical influences on heart rate variability measures were chosen for multiple linear regression analysis. Normative values, derived from a subset of healthy subjects, were adjusted for age and heart rate.

Results. There were 2,722 eligible subjects with a mean age (±SD) of 55 ± 14 years. Three separate multiple linear regression analyses revealed that higher heart rate, older age, beta-adrenergic blocking agent use, history of myocardial infarction or congestive heart failure, diuretic use, diastolic blood pressure ≥ 90 mm Hg, diabetes mellitus, consumption of three or more cups of coffee per day and smoking were associated with lower values of one or more heart rate variability measures, whereas longer processed time, start time in the morning, frequent supraventricular and ventricular premature beats, female gender and systolic blood pressure ≥160 mm Hg were associated with higher values. Age and heart rate were the major determinants of all three selected heart rate variability measures (partial R2 values 0.125 to 0.389). Normative reference values for all eight heart rate variability measures are presented.

Conclusions. Age and heart rate must be taken into account when assessing heart rate variability. [1]

Heart Rate Monitoring

Over the last 20 years, heart rate monitors (HRMs) have become a widely used training aid for a variety of sports. The development of new HRMs has also evolved rapidly during the last two decades. In addition to heart rate (HR) responses to exercise, research has recently focused more on heart rate variability (HRV). Increased HRV has been associated with lower mortality rate and is affected by both age and sex. During graded exercise, the majority of studies show that HRV decreases progressively up to moderate intensities, after which it stabilises. There is abundant evidence from cross-sectional studies that trained individuals have higher HRV than untrained individuals. The results from longitudinal studies are equivocal, with some showing increased HRV after training but an equal number of studies showing no differences. The duration of the training programmes might be one of the factors responsible for the versatility of the results. [2]

Heart rate variability: a review

Heart rate variability (HRV) is a reliable reflection of the many physiological factors modulating the normal rhythm of the heart. In fact, they provide a powerful means of observing the interplay between the sympathetic and parasympathetic nervous systems. It shows that the structure generating the signal is not only simply linear, but also involves nonlinear contributions. Heart rate (HR) is a nonstationary signal; its variation may contain indicators of current disease, or warnings about impending cardiac diseases. The indicators may be present at all times or may occur at random—during certain intervals of the day. It is strenuous and time consuming to study and pinpoint abnormalities in voluminous data collected over several hours. Hence, HR variation analysis (instantaneous HR against time axis) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system. Computer based analytical tools for in-depth study of data over daylong intervals can be very useful in diagnostics. Therefore, the HRV signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. In this paper, we have discussed the various applications of HRV and different linear, frequency domain, wavelet domain, nonlinear techniques used for the analysis of the HRV. [3]

Fetal Heart Rate Interpretation in the Second Stage of Labour: Pearls and Pitfalls

It is vital to determine whether a fetus is showing a normal physiological response to the stress of labour or if the fetus is exposed to intrapartum hypoxia to ensure timely and appropriate management. Failure to interpret fetal heart rate correctly during second stage of labour may lead to increased maternal and neonatal morbidity due to an unnecessary caesarean section or an instrumental vaginal delivery. Conversely, delay in timely and appropriate intervention can also result in increased perinatal morbidity and mortality.

This review addresses the pathophysiology behind features observed on the CTG trace as well as the types of intrapartum hypoxia during second stage of labour and aims to identify common pitfalls including inadvertent monitoring of maternal heart rate as well as monitoring and interpretation of cardiotocograph of twin pregnancies in the second stage of labour. [4]

Differentiation of Hemodynamics of Top Athletes Depending on Heart Rate Variability after Training

Aims: To predict the functional status of the cardiorespiratory system of athletes based on results of responses to exercise.

Study Design: Case-control study.

Place and Duration of Study: Palace of Sports “Dynamo” in Lviv, between January and February 2016.

Methodology: 32 qualified waterpolo male athletes aged 20.6±3.0 years were examined. The research included the study of physical parameters, HR and BP by using routine methods and changes of these parameters during the first 3 minutes after the Martinet Test (1 hour before training) and also the study of cardiorespiratory system using SACR before and during the first 5 minutes after training in state of relative relax in the sitting position. To assess the research results we have used the distribution-free method of statistical analysis, using which we can evaluate the Wilcoxon and Mann-Whitney criteria, and also percentile method analysis based on determining the individual assessments of each indicators that take into consideration falling in appropriate limits of percentile ranges.

Results: Hypokinetic type of hemodynamic is observed in 64% of athletes (EG2) and in 88.2% of athletes (EG1). According to the parameters of central hemodynamic, describing the size of the left ventricle in athletes from EG2, significantly greater (p<0.01) is the end-diastolic volume (EDV)-116.3(107.1;118.8) cm3 and end-systolic volume (ESV)-37.2 (33.9;39.2) cm3 comparing to EDV 92.5(87.0;107.6) cm3 and ESV 27.1(22.4;33.7) cm3 in EG1. Significantly larger (p<0.05) was a stroke volume 78.7(72.5;79.8) cm3 comparing to 64.9 (61.6;77.1) cm3 in EG1.The rate of α-factor that characterizes the BRS and predicts the effectiveness of the regulation of cardiac pump function was significantly higher (p<0.01) with EG2: BRSLF: 19.8(17.3;22.1) versus 10.7(8.7;17.5), BRSHF in EG2: 25.4(17.0;29.7) comparing to 12.8(8.9;24.9) in EG1.

Conclusion: The research revealed that the mentioned features of changes in heart rate variability in the high-frequency range after training have rather accurate determinants in hemodynamic securing an athlete, which in turn can be used to predict and adequately assess the state of the athlete in the recovery period after the competition. [5]


[1] Tsuji, H., Venditti, F.J., Manders, E.S., Evans, J.C., Larson, M.G., Feldman, C.L. and Levy, D., 1996. Determinants of heart rate variability. Journal of the American College of Cardiology, 28(6), pp.1539-1546.

[2] Achten, J. and Jeukendrup, A.E., 2003. Heart rate monitoring. Sports medicine, 33(7), pp.517-538.

[3] Acharya, U.R., Joseph, K.P., Kannathal, N., Lim, C.M. and Suri, J.S., 2006. Heart rate variability: a review. Medical and biological engineering and computing, 44(12), pp.1031-1051.

[4] McDonnell, S. and Chandraharan, E. (2018) “Fetal Heart Rate Interpretation in the Second Stage of Labour: Pearls and Pitfalls”, Journal of Advances in Medicine and Medical Research, 7(12), pp. 957-970. doi: 10.9734/BJMMR/2015/17022.

[5] Guzii, O. and Romanchuk, A. (2017) “Differentiation of Hemodynamics of Top Athletes Depending on Heart Rate Variability after Training”, Journal of Advances in Medicine and Medical Research, 22(3), pp. 1-10. doi: 10.9734/JAMMR/2017/33619.

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